Existing opinion mining studies have focused on and explored only two types of reviews, that is, regular and comparative. There is a visible gap in determining the useful review types from customers and designers perspective. Based on Technology Acceptance Model (TAM) and statistical measures we examine users' perception about different review types and its effects in terms of behavioral intention towards using online review system. By using sample of users (N = 400) and designers (N = 106), current research work studies three review types, A (regular), B (comparative), and C (suggestive), which are related to perceived usefulness, perceived ease of use, and behavioral intention. The study reveals that positive perception of the use of suggestive reviews improves users' decision making in business intelligence. The results also depict that type C (suggestive reviews) could be considered a new useful review type in addition to other types, A and B.
Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.
Curcuminoids originating from turmeric roots are renowned for their diverse pharmacological applications, particularly as a natural anticancer agent. Unfortunately, harnessing the full potential of curcumin derivatives in cancer therapy has been impeded by its inherent limitations, specifically instabilities owing to poor solubility, leading to low systemic bioavailability under normal physiological circumstances. To circumvent this, a novel organic-based drug delivery system employing physically adsorbed β-cyclodextrin (βCD) as an excipient was developed in this study. This resulted in improved aqueous dispersion coupled with anticancer enhancements of tetrahydrocurcumin (THC) at a molar ratio of 2:1. Encapsulation of this agent was confirmed by physicochemical characterisation using UV-vis spectroscopy, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and both in vitro and in vivo approaches. Through the presence of an inclusion complex, a higher aqueous dispersion (65-fold) resulting in a higher drug content and an elevated release profile was achieved. Athymic nude (Nu/Nu) mice exposed to this treatment displayed improvements in tumour regression compared to stand-alone agents, consistent with in vitro cytotoxicity assays with an SI value > 10. The inclusion complex further enhanced apoptosis, as well as anti-migration and anti-invasion rates. Mechanistically, this formulation was consistent in terms of caspase 3 activation. Furthermore, the inclusion complex exhibited reduced systemic toxicity, including reduced inflammation in vital organs as examined by hematoxylin and eosin (H&E) staining. This study also revealed a notable sequential reduction in serum levels of tumour markers, including carcinoembryonic antigen (CEA) and mouse Cytochrome P450 1A2 (CYP1A2), correlating with a significant decrease in tumour bulk volume upon treatment commencement. These compelling findings highlight the potential of this formulation to empower insoluble or poorly soluble hydrophobic agents, thus offering promising prospects for their effective utilisation in colorectal cancer (CRC) chemotherapy.